The approval and pricing process for loans and insurance has evolved into a complicated decision making process. The scope of loans covered could include, but is not be limited to, credit card, overdraft checking protection, term loans, revolving credit lines, automobile loans (both direct and indirect), mortgages, and small business loans. Types of insurance policies include, but are not restricted to, automobile, renters hazard and theft, homeowners, income continuation, life, accident, and umbrella coverage. Many variables have to be considered when making an approval and/or pricing decision, or in negotiating terms and conditions. To assist in these approvals and pricing/negotiation processes, many different approaches have been used. For example, a technique known as dynamic programming can be used which solves the problem of analyzing multiple relationships by dividing the problem into “decision stages,” working backwards from the stated objectives, solving the simplest stages first, and assembling the individual stages into a complete system only after independently solving all of the intermediate stages.
Within this disclosure, computer-implemented systems and methods are disclosed that relate to processes, which construct an empirically derived and statistically based risk evaluation and policy formulation system. For example, a process can be configured so as to accept as input an information base in computer readable form and produce either a single or multistage system composed of alternative decision making strategies. For purposes of illustration, loan approval is used as the primary example. The same methodology applies also to insurance underwriting, loan and insurance policy pricing. The methodology also allows for different approval and pricing strategies based on variations in loan or insurance policy terms and conditions. In the case of loans, this would involve factors such as loan amount tier, the term of the contract, pre-payment penalty, income documentation requirements, etc. On the insurance side, such factors as policy deductible amount, scope of coverage, valuation method for reimbursement on loss of covered items, maximum amount of payout, etc.
As another example, a system and method can be configured to evaluate risks associated with alternative strategies for assessing an entity with respect to a predetermined objective. The system and method can include an action table containing a plurality of possible actions that can be taken with respect to the predetermined objective for various entity profiles. The action table contains an action (e.g., decision) for each entity profile. One or more statistical data stores are configured to contain risk amounts associated with each entity profile and to contain inferred percentage distribution of applicants associated with each entity profile. A profile identification data store contains entity identification information for use in determining an action for the entity. An action is determined for the entity by comparing characteristics associated with the entity with characteristics associated with the entity profiles contained in the action table. An improvement data store provides an indication in the improvement in risk based upon a change in one or more characteristics for an entity profile.
As yet another example, a system and method can be configured to also include a protected class data store that provides an indication of the impact of a policy change on a particular protected class, both with respect to the approval, collection, or promotion decision and relative to one or more characteristics, or combinations of characteristics, for an entity profile.